Optimizating Drilling Operating Parameters with Real-Time Surveillance and Mitigation System of Downhole Vibration in Deep Wells

2016 ◽  
Author(s):  
Fei Zhao ◽  
Haige Wang ◽  
Meng Cui
Keyword(s):  
Real Time ◽  
Operating Parameters ◽  
Deep Wells

Maximizing Drilling Performance with Real-Time Drilling Vibration Mitigation in the Deep Wells

2016 ◽  
Author(s):  
M. Cui ◽  
G. H. Wang ◽  
H. Y. Ge ◽  
X. Z. Chen ◽  
H. W. Guo
Keyword(s):  
Real Time ◽  
Vibration Mitigation ◽  
Drilling Performance ◽  
Deep Wells

Influence of Wild Blueberry Fruit Yield, Plant Height, and Ground Slope on Picking Performance of a Mechanical Harvester: Basis for Automation

2017 ◽  
Vol 33 (5) ◽  
pp. 655-666
Author(s):  
Aitazaz Ahsan Farooque ◽  
Qamar Uz Zaman ◽  
Travis Esau ◽  
Young Ki Chang ◽  
Arnold Walter Schumann ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Real Time ◽  
Plant Height ◽  
Fruit Yield ◽  
Operating Parameters ◽  
Profit Margins ◽  
Ground Speed ◽  
Ground Slope ◽  
Head Height ◽  
Wild Blueberry

Abstract. Spatial variability in fruit losses in relation to fruit yield, plant height, and ground slope can help to automate the wild blueberry harvester to improve picking performance. Currently, harvester operators adjust harvester’s head height, ground speed, and revolutions per minute (rpm) manually. This is not only laborious but also stressful for operators, as they encounter spatial variability during harvesting. The goal of this work was to identify the automation potential of the harvester to improve harvestable yield and reduce operator’s stress, keeping in view the spatial variability. Two fields were selected and test plots were constructed to examine the performance of the harvester in five zones of plant height, fruit yield, and ground slope. Fruit yield plant height and ground slope were recorded from each plot manually to examine their impact on total fruit loss. Keywords: Automation, Fruit losses, Spatial variability, Wild blueberry, Zonal analysis.Results confirmed significant variability in fruit yield, plant height, and ground slope. Fruit losses were significantly influenced by the spatial variations. Fruit losses increased with an increase in fruit yield and ground slope during mechanical harvesting. The picking performance of the blueberry harvester was significantly lower in short and very tall plants within selected fields. The dependence of fruit losses on fruit yield, plant height, and ground slope emphasize the need for real-time adjustments in machine operating parameters to improve berry recovery. Based on the results, it is concluded that there is a significant advantage of harvester’s automation to increase profit margins for growers with no additional cost. Keywords: Automation, Fruit losses, Spatial variability, Wild blueberry, Zonal analysis.

A New Solution to an Old Problem: Tractored CT Conveyed Real-Time Water Injection Profiling in Ultra Deep Wells

2013 ◽  
Author(s):  
James Arukhe ◽  
Saleh Al Ghamdi ◽  
Mubarak Dhufairi ◽  
Laurie Duthie ◽  
Karam Yateem ◽  
...  
Keyword(s):  
Real Time ◽  
Water Injection ◽  
Deep Wells

Intelligent Fault Diagnosis Method Based on Operating Parameters in Nuclear Power Plant

2017 ◽  
Author(s):  
Zhiwu Ke ◽  
Xu Hu ◽  
Dawei Teng ◽  
Mo Tao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Real Time ◽  
Bp Neural Network ◽  
Nuclear Power ◽  
Operating Parameters ◽  
Feed Water ◽  
Mechanical Equipment ◽  
Single Fault ◽  
Diagnosis Method

The safety of mechanical equipment is more important, it directly determines the safety of nuclear power plant operation, and even nuclear safety. So it is necessary to monitor the operating state of NPP system and mechanical equipment in real time by inspecting operating parameters. However, the key technology is real-time fault diagnosis of the mechanical equipment in NPP. Traditional fault diagnosis method based on analytic model is difficult to diagnose relevant and superimposed fault because of model error, disturbance and noise. This paper studies the application of fault diagnosis method based on BP neural network in NPP, and proposes an improved method for neural BP network method. For the feed-water system in the variable load operation process, we select the normal operation, the single feed-water valve fault, feed-water pump and feed-water valve superimposed fault as the analysis objects. One hundred points of data are extracted as BP algorithm training elements in these three processes averagely. The normal and abnormal conditions (including single fault and superimposed fault) can be accurately judged, but the single fault and superimposed failure would produce miscarriage of justice, about 2.4% of the single fault is diagnosed as superimposed fault, the diagnosis time delay is less than 1 second. These results meet the accuracy and real-time requirements. Then we study the application of support vector machine (SVM), which can make up for the deficiency of BP neural network. The results of this paper are useful for the real-time and reliable fault diagnosis of NPP.

On the Development of a PM2.5 Monitoring Network for Real-time Measurements in Urban Environments

2017 ◽  
Vol 68 (4) ◽  
pp. 796-801 ◽  
Author(s):  
Tom Savu ◽  
Bogdan Alexandru Jugravu ◽  
Daniel Dunea
Keyword(s):  
Web Services ◽  
Real Time ◽  
Monitoring Network ◽  
Urban Environments ◽  
Operating Parameters ◽  
Network System ◽  
Preliminary Information ◽  
Successful Design ◽  
Design And Testing ◽  
The Web

A monitoring network system was designed and developed to be deployed in two Romanian cities during the ROkidAIR project for measuring in real time the PM2.5 concentrations. The system comprises 8 stations that were built following the successful design and testing of a PM2.5 optical instrument prototype. The System�s Web services, the data acquisition from the monitoring stations, and the data sending to a GIS-integrated geoportal that includes a decision support system, were firstly evaluated for testing their effectiveness and for eventually defining necessary corrections. After describing the station�s structure and the Web services� main functions, the paper presents data about the most important measured operating parameters of the stations, results from data processing, and conclusions for further developments. Preliminary information collected from a reference gravimetric sampler installed for data inter-comparison with the developed stations is also presented.

Design and Implementation of the AMT Data Acquisition System Structure and Function

2013 ◽  
Vol 712-715 ◽  
pp. 2229-2233
Author(s):  
Yu Cheng ◽  
Hong Wei Zhao ◽  
Song Wang ◽  
Wei Lv ◽  
Tao Sun
Keyword(s):  
Control System ◽  
Data Acquisition ◽  
Real Time ◽  
Output Signal ◽  
Automatic Transmission ◽  
Data Acquisition System ◽  
System Structure ◽  
Acquisition System ◽  
Bench Test ◽  
Operating Parameters

Based on electro-hydraulic control automatic transmission, in order to meet the shifting actuator control effect high response speed, high control accuracy requirements and real-time, accurate acquisition transmission work state. According to the proportional pressure solenoid valve, proportional flow valve response curve and rated operating parameters, designed the hardware control circuit. For the acquisition of automatic transmission shift the implementing agencies cylinder pressure, hydraulic oil temperature and transmission input, output shaft speed, designed the hardware circuit, according to the nominal operating parameters for each sensor. Acquisition speed and accuracy requirements for the quality of the output signal of the respective sensors and control system, designed for each of the output signal of the signal processing and analysis circuit. The bench test the experimental results show that the data acquisition system designed in this paper can meet the requirements of the control system for real-time and accuracy.

A New Solution to an Old Problem: Tractored CT Conveyed Real-Time Water Injection Profiling in Ultra Deep Wells

2013 ◽  
Author(s):  
James Arukhe ◽  
Saleh Al Ghamdi ◽  
Mubarak Dhufairi ◽  
Laurie Duthie ◽  
Karam Yateem ◽  
...  
Keyword(s):  
Real Time ◽  
Water Injection ◽  
Deep Wells

Recurrent neural networks for real-time prediction of TBM operating parameters

2019 ◽  
Vol 98 ◽  
pp. 225-235 ◽  
Author(s):  
Xianjie Gao ◽  
Maolin Shi ◽  
Xueguan Song ◽  
Chao Zhang ◽  
Hongwei Zhang
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Recurrent Neural Networks ◽  
Operating Parameters ◽  
Time Prediction
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